ObjectiveThis paper aims to investigate and evaluate the staged efficacy and safety of the representative empirical prescription of the “Zhu Ke Jiao” theory， Qijia Rougan prescription， combined with entecavir in the treatment of hepatic fibrosis in chronic hepatitis B. MethodsA multicenter randomized controlled clinical study was conducted， and 101 patients diagnosed with chronic hepatitis B-related hepatic fibrosis （CHB-HF） who met the diagnosis and inclusion criteria were randomly assigned to an observation group （Qijia Rougan prescription + entecavir） and a control group （entecavir）. The treatment duration was 24 weeks. Liver stiffness measurement （LSM）， fibrosis-4 index （FIB-4）， portal vein diameter， hepatitis B serology， biochemical indicators， hepatic fibrosis markers in serum ［hyaluronic acid （HA）， laminin （LN）， procollagen Ⅲ peptide （PⅢP）， and type Ⅳ collagen （Ⅳ-C）］， and traditional Chinese medicine syndrome scores were used as efficacy evaluation indicators. Efficacy assessments and explorations of different staged subgroups of Qijia Rougan prescription were conducted according to LSM values based on the Metavir pathological staging standard. ResultsA total of 98 cases were included for statistical analysis， with 49 cases in the observation group and 49 in the control group. The general data of the patients in both groups were comparable. Compared with the same group before treatment， the observation group showed a significant reduction in LSM and FIB-4 （P<0.01）， as well as notable improvements in LN， Ⅳ-C， and various TCM syndrome scores （P<0.05， P<0.01）. When compared to the control group after treatment， the observation group demonstrated significant improvements in LSM， FIB-4， and various TCM syndrome score indicators （P<0.05， P<0.01）， indicating that the observation group performed better than the control group. Subgroup analysis of the regression of hepatic fibrosis stages showed that compared to the same group before treatment， the observation group had better improvement in regression of stages F2 and F3 （P<0.05）. When compared to the control group after treatment， the observation group exhibited superior improvement in regression of stage F3 （P<0.05）. No adverse events occurred in either group during the treatment period. ConclusionCompared with entecavir alone， the combination of Qijia Rougan prescription and entecavir significantly improves the degree of hepatic fibrosis and clinical TCM symptoms in patients. The optimal intervention period is primarily during stage F3， which is a potential “interception” point of the “Zhu Ke Jiao” theory.

BACKGROUND:Eucommia ulmoides has a certain osteogenic effect,which can promote the proliferation and differentiation of osteoblasts.However,it is unclear whether Eucommia ulmoides has effects on alveolar bone formation and Wnt/β-Catenin signaling pathway. OBJECTIVE:To investigate the mechanism by which Eucommia ulmoides promotes alveolar bone formation in ovariectomized rats based on the Wnt/β-Catenin signaling pathway. METHODS:Sixty female Sprague-Dawley rats were selected and randomly divided into five groups:blank control group,sham-operation group,model group,low-dose group Eucommia ulmoides group,and high-dose Eucommia ulmoides group,with twelve rats in each group.Osteoporosis animal models were constructed by bilateral oophorectomy in the model group and the low-dose and high-dose Eucommia ulmoides groups.The sham-operation group underwent the same method to remove adipose tissue of equal mass around the bilateral ovaries.Three months after surgery,the low-and high-dose Eucommia ulmoides groups were given 2.1 g/kg/d and 4.2 g/kg/d Eucommia ulmoides by gavage,respectively.The sham-operation group and model group were given the same amount of physiological saline by gavage.After 12 weeks of drug intervention,the changes in alveolar bone mass of rats in each group were observed through Micro-CT;hematoxylin-eosin staining was used to observe the pathological structural changes of alveolar bone in rats;enzyme linked immunosorbent assay was used to detect the expression levels of alkaline phosphatase and osteocalcin in the serum of rats;western blot was used to detect the expression levels of β-Catenin and Frizzled9 receptor proteins in the alveolar bone of rats;and real-time fluorescence quantitative PCR was used to detect the expression of osteocalcin,Runt-related transcription factor 2(Runx2),alkaline phosphatase,β-catenin,and frizzled9 mRNAs in alveolar bone tissues of rats. RESULTS AND CONCLUSION:Compared with the blank control group,bone volume fraction,trabecular number,trabecular thickness,and bone mineral density were reduced in the model group(P＜0.05),and trabecular separation was elevated(P＜0.05).Pathological observation showed that the arrangement of trabeculae was disordered and irregular,the trabeculae were thinned or broken,and the marrow cavity was enlarged in the model group,with a significant reduction in bone volume;the level of alkaline phosphatase in the serum was increased(P＜0.05),and the level of osteocalcin was decreased(P＜0.05);mRNA expression of alkaline phosphatase,osteocalcin,Runx2,β-catenin,and frizzled9 were decreased(P＜0.05);protein expression of β-Catenin and Frizzled9 was decreased(P＜0.05).Compared with the model group,the low-and high-dose Eucommia ulmoides groups showed an increase in bone volume fraction,trabecular number,trabecular thickness,and bone mineral density(P＜0.05)and a decrease in trabecular separation(P＜0.05).In the low-and high-dose Eucommia ulmoides groups,bone trabeculae were slightly aligned and thickened,with a significant increase in bone mass.Compared with the model group,the serum level of alkaline phosphatase was reduced(P＜0.05)and the serum level of osteocalcin was elevated(P＜0.05)in the low-and high-dose Eucommia ulmoides groups.Compared with the model group,the mRNA expression of alkaline phosphatase,osteocalcin,Runx2,β-catenin,and frizzled9 were increased in the low-and high-dose Eucommia ulmoides groups(P＜0.05).Compared with the model group,the protein expression of Frizzled9 was increased in the low-dose Eucommia ulmoides group(P＜0.05),while the protein expression of β-Catenin and Frizzled9 was increased in the high-dose Eucommia ulmoides group(P＜0.05).Compared with the low-dose Eucommia ulmoides group,the high-dose Eucommia ulmoides group had a more significant improvement in the above indexes.To conclude,Eucommia ulmoides can effectively promote the alveolar bone formation,and its mechanism of action might be related to the activation of the Wnt/β-catenin signaling pathway.

Objective:To develop and validate machine learning-based radiomics models using preoperative CT images for individualized prediction of mitotic index (MI) in patients with gastrointestinal stromal tumors (GIST).Methods:The study was a case-control study. The data of 348 GIST patients confirmed by pathology were retrospectively collected from two independent medical centers: the Affiliated Hospital of Qingdao University (center 1) and Shandong Provincial Hospital Affiliated to Shandong First Medical University (center 2), covering the period from January 2013 to June 2018. Patients from center 1 were divided into a training cohort (176 cases) and an internal validation cohort (75 cases) at a ratio of 7∶3 using random sampling. Patients from center 2 served as an independent external validation cohort (97 cases). The primary endpoint was MI, categorized into high MI (145 cases) and low MI (203 cases) groups. Radiomic features were extracted from the portal venous phase images of preoperative contrast-enhanced CT scans. Five machine learning algorithms, including logistic regression, support vector machine, random forest, decision tree, and extreme gradient boosting (XGBoost),were employed to construct MI prediction models. The optimal model was identified using receiver operating characteristic curves. An individualized prediction model was developed by integrating the the optimal machine learning model combined with selected independent clinical factors, and the importance of features was visualized using Shapley Additive Explanation (SHAP) analysis. Patients were followed up, and Kaplan-Meier curves along with log-rank tests were used to evaluate recurrence-free survival (RFS) differences between the predicted high MI and low MI groups.Results:Among the five constructed machine learning models, the XGBoost model demonstrated the best predictive performance, with area under the curve (AUC) of 0.809 (95% CI 0.738-0.872), 0.693 (95% CI 0.571-0.809), and 0.718 (95% CI 0.605-0.822) in the training cohort, internal validation cohort, and external validation cohort, respectively. An individualized prediction model combining the XGBoost model with independent clinical factors (tumor location and tumor size) was developed. The model achieved AUC of 0.843 (95% CI 0.785-0.899), 0.791 (95% CI 0.680-0.894), and 0.777 (95% CI 0.678-0.861) in the training cohort, internal validation cohort, and external validation cohort, respectively. SHAP analysis indicated that radiomic features had the highest predictive impact. In both the training cohort and internal validation cohort, the RFS of patients predicted to be in the high MI group was lower than that of the low MI group, with statistically significant differences ( χ2=14.58, 9.52, both P<0.001). However, there was no statistically significant difference in RFS in the external validation set ( χ2=6.18, P=0.080). Conclusions:The optimal XGBoost model based on radiomic features extracted from preoperative portal venous phase CT images, when combined with clinical factors, can effectively predict the MI of GIST patients.

Objective To investigate the current situation of frailty in patients with home peritoneal dialysis and analyze its influencing factors,so as to provide bases for early identification of high-risk groups and targeted clinical intervention.Methods From October to December 2024,205 home-based peritoneal dialysis patients under long-term follow-up at a tertiary A hospital in Zhongshan,Guangdong Province,were selected by convenience sampling.The General Information Questionnaire,Fried Phenotype Scale,Nutritional Risk Screening Tool,Exercise Self-Efficacy Scale,and Social Support Rating Scale were used to investigate the influencing factors of frailty in home-based peritoneal dialysis patients by ordinal logistic regression analysis.Results A total of 201 valid questionnaires were collected.The prevalence of frailty among home-based peritoneal dialysis patients was 24.3％,while 39.3％were in the pre-frailty stage,and 36.3％showed no frailty.0rdinal logistic regression analysis revealed that age,residence location,primary disease,sleep status,serum albumin concentration,and exercise self-efficacy level were influencing factors of frailty in the home peritoneal dialysis patients(P＜0.05).Conclusion The incidence of frailty in patients with home peritoneal dialysis is higher,and patients with advanced age,living in rural areas,other types of primary diseases,insomnia,low serum albumin concentration and low exercise self-efficacy are more likely to develop frailty.Specialist doctors and nurses of peritoneal dialysis should pay attention to the early screening of frailty in patients with peritoneal dialysis at home,and take personalized intervention measures to prevent or delay the occurrence of frailty according to the relevant risk factors.

Objective:To develop and validate machine learning-based radiomics models using preoperative CT images for individualized prediction of mitotic index (MI) in patients with gastrointestinal stromal tumors (GIST).Methods:The study was a case-control study. The data of 348 GIST patients confirmed by pathology were retrospectively collected from two independent medical centers: the Affiliated Hospital of Qingdao University (center 1) and Shandong Provincial Hospital Affiliated to Shandong First Medical University (center 2), covering the period from January 2013 to June 2018. Patients from center 1 were divided into a training cohort (176 cases) and an internal validation cohort (75 cases) at a ratio of 7∶3 using random sampling. Patients from center 2 served as an independent external validation cohort (97 cases). The primary endpoint was MI, categorized into high MI (145 cases) and low MI (203 cases) groups. Radiomic features were extracted from the portal venous phase images of preoperative contrast-enhanced CT scans. Five machine learning algorithms, including logistic regression, support vector machine, random forest, decision tree, and extreme gradient boosting (XGBoost),were employed to construct MI prediction models. The optimal model was identified using receiver operating characteristic curves. An individualized prediction model was developed by integrating the the optimal machine learning model combined with selected independent clinical factors, and the importance of features was visualized using Shapley Additive Explanation (SHAP) analysis. Patients were followed up, and Kaplan-Meier curves along with log-rank tests were used to evaluate recurrence-free survival (RFS) differences between the predicted high MI and low MI groups.Results:Among the five constructed machine learning models, the XGBoost model demonstrated the best predictive performance, with area under the curve (AUC) of 0.809 (95% CI 0.738-0.872), 0.693 (95% CI 0.571-0.809), and 0.718 (95% CI 0.605-0.822) in the training cohort, internal validation cohort, and external validation cohort, respectively. An individualized prediction model combining the XGBoost model with independent clinical factors (tumor location and tumor size) was developed. The model achieved AUC of 0.843 (95% CI 0.785-0.899), 0.791 (95% CI 0.680-0.894), and 0.777 (95% CI 0.678-0.861) in the training cohort, internal validation cohort, and external validation cohort, respectively. SHAP analysis indicated that radiomic features had the highest predictive impact. In both the training cohort and internal validation cohort, the RFS of patients predicted to be in the high MI group was lower than that of the low MI group, with statistically significant differences ( χ2=14.58, 9.52, both P<0.001). However, there was no statistically significant difference in RFS in the external validation set ( χ2=6.18, P=0.080). Conclusions:The optimal XGBoost model based on radiomic features extracted from preoperative portal venous phase CT images, when combined with clinical factors, can effectively predict the MI of GIST patients.

Objective To investigate the current situation of frailty in patients with home peritoneal dialysis and analyze its influencing factors,so as to provide bases for early identification of high-risk groups and targeted clinical intervention.Methods From October to December 2024,205 home-based peritoneal dialysis patients under long-term follow-up at a tertiary A hospital in Zhongshan,Guangdong Province,were selected by convenience sampling.The General Information Questionnaire,Fried Phenotype Scale,Nutritional Risk Screening Tool,Exercise Self-Efficacy Scale,and Social Support Rating Scale were used to investigate the influencing factors of frailty in home-based peritoneal dialysis patients by ordinal logistic regression analysis.Results A total of 201 valid questionnaires were collected.The prevalence of frailty among home-based peritoneal dialysis patients was 24.3％,while 39.3％were in the pre-frailty stage,and 36.3％showed no frailty.0rdinal logistic regression analysis revealed that age,residence location,primary disease,sleep status,serum albumin concentration,and exercise self-efficacy level were influencing factors of frailty in the home peritoneal dialysis patients(P＜0.05).Conclusion The incidence of frailty in patients with home peritoneal dialysis is higher,and patients with advanced age,living in rural areas,other types of primary diseases,insomnia,low serum albumin concentration and low exercise self-efficacy are more likely to develop frailty.Specialist doctors and nurses of peritoneal dialysis should pay attention to the early screening of frailty in patients with peritoneal dialysis at home,and take personalized intervention measures to prevent or delay the occurrence of frailty according to the relevant risk factors.

Objective To observe the value of ultrasound combined with hematologic tests for diagnosing acute complicated appendicitis.Methods Data of 225 patients with acute appendicitis confirmed by surgery and postoperative pathology were retrospectively analyzed.The patients were divided into acute complicated appendicitis group(complicated group,n=33)and acute uncomplicated appendicitis group(uncomplicated group,n=192)based on operational and post operation pathological findings.Clinical data,hematologic test results and ultrasound findings before operation were compared between groups.Multivariate logistic regression was used to construct a combination model for diagnosing acute complicated appendicitis.Receiver operating characteristic(ROC)curve and area under the curve(AUC)were used to evaluate the diagnostic value of predictive factor alone and their combination for diagnosing acute complicated appendicitis,which were then compared with DeLong test.Results Compared with uncomplicated group,patients in complicated group were older,with higher proportion of fever and vomiting,higher level of C reactive protein(CRP)and of neutrophil-to-lymphocyte ratio(NLR),larger external diameters of appendix,also higher proportion of appendiceal intracavitary fecalith and periappendiceal abscess(all P＜0.05).Multivariate logistic regression analysis showed that elevated serum CRP and NLR,increased external diameter of appendix and periappendiceal abscess were all predictive factors of acute complicated appendicitis(all P＜0.05).The diagnostic efficiency of logistic regression model constructed based on the above factors(AUC=0.854)was higher than that of each single predictive factor alone(Z=2.548-4.527,all P＜0.05).Conclusion Ultrasound combined with hematologic tests had high value for diagnosing acute complicated appendicitis.

Objective:To investigate the characteristics of the endometrial microbiota in patients with varying degrees of intrauterine adhesion (IUA).Methods:This single-center cross-sectional observational study enrolled 115 patients with IUA who were treated at the Hysteroscopic Center of Fuxing Hospital, Capital Medical University, from May 2022 to October 2023. After quality control and data preprocessing, 81 samples met the inclusion criteria for analysis. Patients were grouped according to an established IUA scoring and grading system into mild IUA ( n=38) and moderate-to-severe IUA ( n=43). Endometrial tissue was collected under sterile conditions. Bacterial genomic DNA was extracted, the 16S rRNA V3-V4 region was amplified, and sequencing was performed on an Illumina platform. Differences in endometrial microbiota diversity and composition were compared between the two groups. Results:Patients with varying degrees of IUA exhibited comparable species richness, evenness and diversity of endometrial microbiota. At the phylum level, the endometrial microbiota across all subjects was predominantly composed of Proteobacteria, Firmicutes, Cyanobacteriota, Bacteroidota, and Actinobacteriota, with Proteobacteria (32.29%) and Firmicutes (23.82%) showing the highest mean relative abundances. At the genus level, Ralstonia (16.67%), Lactobacillus (13.45%), and Streptococcus (7.07%) were the most abundant genera. Group comparisons showed that the abundance of Ralstonia was higher in the mild IUA group, whereas Lactobacillus, Vibrio and Pseudoalteromonas were more abundant in the moderate-to-severe IUA group; however, these differences did not reach statistical significance (all P>0.05). LEfSe analysis further indicated that Lactobacillus, Vibrio, Pseudoalteromonas, Aeromonas, Ureaplasma and Acetobacterium were relatively enriched in the moderate-to-severe IUA group, while Geobacillus, Stomatobaculum and Fusicatenibacter were more abundant in the mild IUA group. Conclusion:The composition of the endometrial microbiota differs among patients with varying IUA severity. IUA progression may be associated with alterations in the endometrial microbiota; however, causal relationships and underlying mechanisms require further investigation.

Objective:To establish a rapid and precise dose measurement method with EBT4 film and ensure its measurement accuracy to be within the required range through strict operational procedures for the purpose of addressing the two essential issues of poor measurement accuracy and timeliness of EBT film under FLASH conditions.Methods:After storing under different humidity conditions for a certain period of time, the film was exposed to radiation for analyzing the influence of air humidity on the intrinsic performance of EBT film. By means of repeated scanning operations and the film angle rotation, the influences of repeated scanning and film placement angle were analuzed. Parabolic correction method was used to reduce the spatial position influence during the scanning process. By analying the relationship between net optical density (netOD) and absorbed dose through the comparison of three fitting method, the optimal fitting curve was selected. After irradiation of the same batch of films for 5 min and 24 h, the film doses were calibrated and then compared with ionization chamber-measured result. The rapid and precise film dosimetry method was used to measure both the percentage depth dose from X-rays at ultra-high dose rate and the dose distribution at a depth of 2 cm in water.Results:Air humidity had the greatest influence on the intrinsic performance of EBT film (approximately 20%). The average deviation of repeated scans is within 0.5%. The angle at which the film is placed significantly affected the readouts of the film with the maximum influence approximately 70%. The net optical density combined with polynomial fitting can control the fitting residuals of 1-16 Gy within 3%. The change rate of light channels at 5 min already mostly met the requirements of the rapid mode (< 0.5%). Compared with the measurement result obtained using the reference ionization chamber, the deviations of the 5 min or 24 h dose calibration curves were all within 2%.Conclusions:The EBT4 film can be employed as a precise dosimeter to quickly measure the FLASH radiation dose. Rapid and precise FLASH dose measurements can meet the stringent requirements of both preclinical and clinical FLASH research.

To analyze the clinical data of patients with recurrent Clostridioides difficile infection(rCDI)in the southwestern region,and help clinicians identify high-risk populations early and adopt appropriate treatment strate-gies.Methods Clinical data of rCDI cases from a tertiary first-class hospital in southwest China from July 2019 to June 2024 were collected,and their host-related risk factors,exogenous risk factors,treatment schemes,and end-point outcomes were analyzed.Results In the past five years,a total of 26 cases of rCDI were detected from a ter-tiary first-class hospital in southwest China,accounting for 4.04％of the total positive cases(n=643)detected during the same period.19.23％of the recurrent patients experienced a second recurrence.The median age of rCDI patients was 66.5 years old,with males accounting for 73.08％.The major comorbidity included diabetes mellitus(34.62％),malignant tumors(30.77％),and chronic renal diseases(23.08％).In the 8 weeks before the first on-set of rCDI in patients,the top three drugs used were β-lactam and enzyme inhibitor compound(69.23％),proton pump inhibitors(65.38％),and carbapenems(46.15％).In the initial treatment of recurrent patients,3.85％(1/26)of the cases were treated with metronidazole,19.23％(5/26)of the cases received non-standard vancomy-cin/norvancomycin treatment in usage or dosage,46.15％(12/26)of the cases received treatment with course less than 10 days.In the treatment of the first recurrence,34.78％(8/26)of the cases received treatment with course less than 10 days.Conclusion For elderly patients and patients with diabetes mellitus,malignant tumors,and chronic renal diseases,who have used β-lactam and enzyme inhibitor compound,proton pump inhibitors,carbapene-ms within 8 weeks before disease onset,standard dose and treatment course of vancomycin or norvancomycin are recommended under permitted conditions,so as to reduce the risk of recurrence.